Scheduling%20Periodic%20Maintenance%20of%20Aircraft%20through%20simulation-based%20optimization

1
Scheduling Periodic Maintenance of Aircraft
through simulation-based optimization

• Ville Mattila and Kai Virtanen
• Systems Analysis Laboratory, Helsinki University
  of Technology

2
Contents

• The need for periodic maintenance (PM) scheduling
• Scheduling of PM tasks in the Finnish Air Force
  (FiAF)
• A simulation-based optimization model for the
  scheduling task
• Results from an example scheduling case

3
Aircraft usage and maintenance
Usage
Maintenance
Pilot and tactical training, air surveillance A
number of aircraft chosen each day to flight
duty Several missions during one day
Periodic maintenance Based on usage
Failure repairs Unplanned
Different level maintenance facilities
4
Periodic maintenance of a Hawk Mk51 training
aircraft
Type of PM task Maintenance interval (flight hours) Average duration (hours) Maintenance level
C 50 10 Organizational level (O-level), Squadron
D1, D2 125 to 250 75 to 200 Intermediate level (I-level), Air commands repair shop
E, F, G 500 to 2000 300 to 500 Depot level (D-level), Industrial repair shop
5
The need for PM scheduling

• Scheduling is done for two primary reasons
• Avoid degradation of aircraft availability
• Allow maintenance facilities to plan for supply
  of resources

6
Scheduling vs. no scheduling
7
Difficulty of scheduling

• Starting times of PM tasks can not be assigned
  with certainty
• Timing depends on the maintenance interval and on
  the usage of the aircraft
• Usage is affected by unexpected failures and
  subsequent repairs
• Intervals are not adjusted during normal
  conditions

8
Maintenance schedule

• A maintenance schedule consists of targeted
  starting times of PM tasks
• The schedule is used to allocate flight time
  among aircraft by prioritizing aircraft with the
  highest ratio of
• The allocation governs the accumulation of flight
  hours and the actual timing of PM tasks

9
The maintenance scheduling problem

• N the total number of aircraft
• X(x1,1,...,x1,n1,...,xN,1,...,xN,nN) the
  maintenance schedule of the fleet
• L simulated average aircraft availability
• ? sample path

10
The simulation optimization model

• A discrete-event simulation model
• Describes aircraft usage and maintenance under a
  given maintenance schedule
• Returns aircraft availability as output
• A search method
• Produces new schedules based on the simulated
  availabilities
• A genetic algorithm (GA) or simulated annealing
  (SA)

11
A case example

• The scheduling case
• A fleet of 16 aircraft
• A time period of 1 year
• 4 of the aircraft each perform 4 daily flight
  missions
• 4 PM tasks scheduled per each aircraft in the
  fleet
• The performance of different configurations of GA
  and SA in the case are compared

12
Design of experiment

• 300 evaluations of the simulation for each
  combination of parameters

GA GA GA GA
Population size 10 20 30
Probability of crossover 0.6 0.8 1.0
Amplitude of crossover 1arge medium small
SA SA SA SA
Number of rescheduled tasks per iteration 3 6 9
Amplitude of rescheduling small medium large
Probability of accepting a degrading schedule small medium large
13
Results

• Highest average availability obtained in the
  optimization

GA GA GA GA
Population size 0.636 0.657 0.647
Probability of crossover 0.638 0.646 0.654
Amplitude of crossover 0.668 0.638 0.633
SA SA SA SA
Number of rescheduled tasks per iteration 0.682 0.697 0.726
Amplitude of rescheduling 0.658 0.713 0.733
Probability of accepting a degrading schedule 0.702 0.705 0.698
14
Analysis of the obtained schedule

• The simulation can be used to further assess the
  schedule obtained in the optimization
• The queuing times in the maintenance facilities
  indicate whether the schedule can still be
  improved
• The simulation also provides information on the
  distribution of times, when the PM tasks are
  actually materialized

15
Concluding remarks

• The presented model has been implemented as a
  design tool for FiAF
• Final validation can be conducted by comparing
  actual flight operations and maintenance with the
  simulation
• Future work includes the consideration of task
  priorities in the optimization problem